Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells6
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory96.3 B

Variable types

Categorical2
Numeric10

Alerts

Lifetime Post Total Reach is highly correlated with Lifetime Post Total Impressions and 6 other fieldsHigh correlation
Lifetime Post Total Impressions is highly correlated with Lifetime Post Total Reach and 7 other fieldsHigh correlation
Lifetime Engaged Users is highly correlated with Lifetime Post Total Reach and 6 other fieldsHigh correlation
Lifetime Post Consumers is highly correlated with Lifetime Post Total Reach and 5 other fieldsHigh correlation
Lifetime Post Consumptions is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Comments is highly correlated with Lifetime Post Total Reach and 4 other fieldsHigh correlation
Likes is highly correlated with Lifetime Post Total Reach and 6 other fieldsHigh correlation
Shares is highly correlated with Lifetime Post Total Impressions and 4 other fieldsHigh correlation
Total Interactions is highly correlated with Lifetime Post Total Reach and 6 other fieldsHigh correlation
Lifetime Post Total Reach is highly correlated with Lifetime Post Total Impressions and 3 other fieldsHigh correlation
Lifetime Post Total Impressions is highly correlated with Lifetime Post Total ReachHigh correlation
Lifetime Engaged Users is highly correlated with Lifetime Post Total Reach and 6 other fieldsHigh correlation
Lifetime Post Consumers is highly correlated with Lifetime Engaged Users and 1 other fieldsHigh correlation
Lifetime Post Consumptions is highly correlated with Lifetime Engaged Users and 1 other fieldsHigh correlation
Comments is highly correlated with Lifetime Engaged Users and 3 other fieldsHigh correlation
Likes is highly correlated with Lifetime Post Total Reach and 4 other fieldsHigh correlation
Shares is highly correlated with Lifetime Engaged Users and 3 other fieldsHigh correlation
Total Interactions is highly correlated with Lifetime Post Total Reach and 4 other fieldsHigh correlation
Lifetime Post Total Reach is highly correlated with Lifetime Post Total Impressions and 3 other fieldsHigh correlation
Lifetime Post Total Impressions is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Lifetime Engaged Users is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Lifetime Post Consumers is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Lifetime Post Consumptions is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Comments is highly correlated with Likes and 1 other fieldsHigh correlation
Likes is highly correlated with Comments and 2 other fieldsHigh correlation
Shares is highly correlated with Likes and 1 other fieldsHigh correlation
Total Interactions is highly correlated with Comments and 2 other fieldsHigh correlation
Lifetime Post Total Reach is highly correlated with Lifetime Post Total Impressions and 6 other fieldsHigh correlation
Lifetime Post Total Impressions is highly correlated with Lifetime Post Total Reach and 3 other fieldsHigh correlation
Lifetime Engaged Users is highly correlated with Lifetime Post Total Reach and 7 other fieldsHigh correlation
Lifetime Post Consumers is highly correlated with Lifetime Post Total Reach and 2 other fieldsHigh correlation
Lifetime Post Consumptions is highly correlated with Lifetime Engaged Users and 1 other fieldsHigh correlation
Comments is highly correlated with Lifetime Post Total Reach and 5 other fieldsHigh correlation
Likes is highly correlated with Lifetime Post Total Reach and 4 other fieldsHigh correlation
Shares is highly correlated with Lifetime Post Total Reach and 5 other fieldsHigh correlation
Total Interactions is highly correlated with Lifetime Post Total Reach and 4 other fieldsHigh correlation
Comments has 106 (21.2%) zeros Zeros
Shares has 13 (2.6%) zeros Zeros
Total Interactions has 6 (1.2%) zeros Zeros

Reproduction

Analysis started2022-04-26 12:14:48.420216
Analysis finished2022-04-26 12:15:03.279386
Duration14.86 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Category
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
215 
3
155 
2
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1215
43.0%
3155
31.0%
2130
26.0%

Length

2022-04-26T13:15:03.352489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T13:15:03.414503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1215
43.0%
3155
31.0%
2130
26.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Post Month
Real number (ℝ≥0)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.038
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:03.486569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.307936046
Coefficient of variation (CV)0.470010805
Kurtosis-1.136445822
Mean7.038
Median Absolute Deviation (MAD)3
Skewness-0.1222623099
Sum3519
Variance10.94244088
MonotonicityDecreasing
2022-04-26T13:15:03.604126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1060
12.0%
752
10.4%
1250
10.0%
450
10.0%
649
9.8%
1145
9.0%
537
7.4%
936
7.2%
336
7.2%
834
6.8%
Other values (2)51
10.2%
ValueCountFrequency (%)
125
5.0%
226
5.2%
336
7.2%
450
10.0%
537
7.4%
649
9.8%
752
10.4%
834
6.8%
936
7.2%
1060
12.0%
ValueCountFrequency (%)
1250
10.0%
1145
9.0%
1060
12.0%
936
7.2%
834
6.8%
752
10.4%
649
9.8%
537
7.4%
450
10.0%
336
7.2%

Paid
Categorical

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
0.0
360 
1.0
139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0360
72.0%
1.0139
 
27.8%
(Missing)1
 
0.2%

Length

2022-04-26T13:15:03.684658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T13:15:03.724820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0360
72.1%
1.0139
 
27.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lifetime Post Total Reach
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13903.36
Minimum238
Maximum180480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:03.807381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum238
5-th percentile1326.55
Q13315
median5281
Q313168
95-th percentile54319.2
Maximum180480
Range180242
Interquartile range (IQR)9853

Descriptive statistics

Standard deviation22740.78789
Coefficient of variation (CV)1.63563253
Kurtosis16.79992731
Mean13903.36
Median Absolute Deviation (MAD)2855.5
Skewness3.679156464
Sum6951680
Variance517143433.8
MonotonicityNot monotonic
2022-04-26T13:15:03.903974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22322
 
0.4%
37542
 
0.4%
52802
 
0.4%
33222
 
0.4%
26452
 
0.4%
52902
 
0.4%
95282
 
0.4%
34142
 
0.4%
33582
 
0.4%
66922
 
0.4%
Other values (475)480
96.0%
ValueCountFrequency (%)
2381
0.2%
3911
0.2%
4521
0.2%
5841
0.2%
6171
0.2%
6191
0.2%
6451
0.2%
6521
0.2%
6591
0.2%
6772
0.4%
ValueCountFrequency (%)
1804801
0.2%
1582081
0.2%
1535361
0.2%
1390081
0.2%
1280641
0.2%
1229441
0.2%
1090561
0.2%
1056321
0.2%
1007681
0.2%
988161
0.2%

Lifetime Post Total Impressions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29585.948
Minimum570
Maximum1110282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:04.020282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum570
5-th percentile2451.7
Q15694.75
median9051
Q322085.5
95-th percentile110238.5
Maximum1110282
Range1109712
Interquartile range (IQR)16390.75

Descriptive statistics

Standard deviation76803.24667
Coefficient of variation (CV)2.595936648
Kurtosis94.00195526
Mean29585.948
Median Absolute Deviation (MAD)4670.5
Skewness8.35100834
Sum14792974
Variance5898738699
MonotonicityNot monotonic
2022-04-26T13:15:04.128692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127352
 
0.4%
87452
 
0.4%
70042
 
0.4%
43722
 
0.4%
85332
 
0.4%
65032
 
0.4%
325761
 
0.2%
113731
 
0.2%
49541
 
0.2%
72781
 
0.2%
Other values (484)484
96.8%
ValueCountFrequency (%)
5701
0.2%
7261
0.2%
7461
0.2%
10291
0.2%
10711
0.2%
10961
0.2%
11171
0.2%
11581
0.2%
12401
0.2%
12851
0.2%
ValueCountFrequency (%)
11102821
0.2%
6657921
0.2%
4979101
0.2%
4575091
0.2%
4532131
0.2%
3191331
0.2%
2771001
0.2%
2522071
0.2%
2512691
0.2%
2297331
0.2%

Lifetime Engaged Users
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct414
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean920.344
Minimum9
Maximum11452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:04.235189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile169.75
Q1393.75
median625.5
Q31062
95-th percentile2581.25
Maximum11452
Range11443
Interquartile range (IQR)668.25

Descriptive statistics

Standard deviation985.016636
Coefficient of variation (CV)1.070270069
Kurtosis34.11123064
Mean920.344
Median Absolute Deviation (MAD)283.5
Skewness4.515919585
Sum460172
Variance970257.7732
MonotonicityNot monotonic
2022-04-26T13:15:04.347459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5374
 
0.8%
2063
 
0.6%
4243
 
0.6%
5173
 
0.6%
5643
 
0.6%
3383
 
0.6%
10623
 
0.6%
4213
 
0.6%
11413
 
0.6%
5413
 
0.6%
Other values (404)469
93.8%
ValueCountFrequency (%)
91
0.2%
151
0.2%
171
0.2%
241
0.2%
251
0.2%
372
0.4%
662
0.4%
841
0.2%
971
0.2%
1031
0.2%
ValueCountFrequency (%)
114521
0.2%
80721
0.2%
61641
0.2%
53521
0.2%
48401
0.2%
45441
0.2%
42581
0.2%
39841
0.2%
38721
0.2%
37421
0.2%

Lifetime Post Consumers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct422
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean798.772
Minimum9
Maximum11328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:04.462219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile123.95
Q1332.5
median551.5
Q3955.5
95-th percentile2319.4
Maximum11328
Range11319
Interquartile range (IQR)623

Descriptive statistics

Standard deviation882.5050131
Coefficient of variation (CV)1.104827176
Kurtosis44.95625301
Mean798.772
Median Absolute Deviation (MAD)264
Skewness5.033074953
Sum399386
Variance778815.0982
MonotonicityNot monotonic
2022-04-26T13:15:04.567597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1823
 
0.6%
6423
 
0.6%
3193
 
0.6%
4823
 
0.6%
3223
 
0.6%
3743
 
0.6%
5033
 
0.6%
5133
 
0.6%
3353
 
0.6%
2983
 
0.6%
Other values (412)470
94.0%
ValueCountFrequency (%)
91
0.2%
151
0.2%
171
0.2%
231
0.2%
251
0.2%
372
0.4%
591
0.2%
631
0.2%
651
0.2%
711
0.2%
ValueCountFrequency (%)
113281
0.2%
59341
0.2%
52021
0.2%
47541
0.2%
41001
0.2%
40101
0.2%
38221
0.2%
36821
0.2%
35861
0.2%
34641
0.2%

Lifetime Post Consumptions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct440
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.13
Minimum9
Maximum19779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:04.810021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile153.95
Q1509.25
median851
Q31463
95-th percentile4540.5
Maximum19779
Range19770
Interquartile range (IQR)953.75

Descriptive statistics

Standard deviation2000.594118
Coefficient of variation (CV)1.413717551
Kurtosis31.37938201
Mean1415.13
Median Absolute Deviation (MAD)420
Skewness4.817636457
Sum707565
Variance4002376.827
MonotonicityNot monotonic
2022-04-26T13:15:04.923084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5993
 
0.6%
7303
 
0.6%
7953
 
0.6%
8893
 
0.6%
7193
 
0.6%
5133
 
0.6%
6523
 
0.6%
4313
 
0.6%
3372
 
0.4%
9132
 
0.4%
Other values (430)472
94.4%
ValueCountFrequency (%)
91
0.2%
191
0.2%
201
0.2%
261
0.2%
311
0.2%
491
0.2%
551
0.2%
701
0.2%
711
0.2%
951
0.2%
ValueCountFrequency (%)
197791
0.2%
181151
0.2%
149741
0.2%
120741
0.2%
110641
0.2%
96141
0.2%
92371
0.2%
84151
0.2%
83081
0.2%
80491
0.2%

Comments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.482
Minimum0
Maximum372
Zeros106
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:05.029450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile25.05
Maximum372
Range372
Interquartile range (IQR)6

Descriptive statistics

Standard deviation21.18090975
Coefficient of variation (CV)2.830915497
Kurtosis183.43997
Mean7.482
Median Absolute Deviation (MAD)3
Skewness11.76756353
Sum3741
Variance448.6309379
MonotonicityNot monotonic
2022-04-26T13:15:05.143180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0106
21.2%
271
14.2%
162
12.4%
444
8.8%
336
 
7.2%
626
 
5.2%
720
 
4.0%
520
 
4.0%
915
 
3.0%
1011
 
2.2%
Other values (36)89
17.8%
ValueCountFrequency (%)
0106
21.2%
162
12.4%
271
14.2%
336
 
7.2%
444
8.8%
520
 
4.0%
626
 
5.2%
720
 
4.0%
88
 
1.6%
915
 
3.0%
ValueCountFrequency (%)
3721
0.2%
1461
0.2%
1441
0.2%
1031
0.2%
641
0.2%
601
0.2%
581
0.2%
561
0.2%
511
0.2%
471
0.2%

Likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct257
Distinct (%)51.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean177.9458918
Minimum0
Maximum5172
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:05.234982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q156.5
median101
Q3187.5
95-th percentile534.1
Maximum5172
Range5172
Interquartile range (IQR)131

Descriptive statistics

Standard deviation323.3987416
Coefficient of variation (CV)1.817399314
Kurtosis119.1824443
Mean177.9458918
Median Absolute Deviation (MAD)54
Skewness8.955312968
Sum88795
Variance104586.7461
MonotonicityNot monotonic
2022-04-26T13:15:05.373289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
987
 
1.4%
796
 
1.2%
536
 
1.2%
726
 
1.2%
76
 
1.2%
1486
 
1.2%
325
 
1.0%
05
 
1.0%
485
 
1.0%
1015
 
1.0%
Other values (247)442
88.4%
ValueCountFrequency (%)
05
1.0%
11
 
0.2%
23
0.6%
33
0.6%
44
0.8%
51
 
0.2%
63
0.6%
76
1.2%
81
 
0.2%
91
 
0.2%
ValueCountFrequency (%)
51721
0.2%
19981
0.2%
16391
0.2%
16221
0.2%
15721
0.2%
15461
0.2%
15051
0.2%
13721
0.2%
11551
0.2%
10471
0.2%

Shares
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct88
Distinct (%)17.7%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean27.26612903
Minimum0
Maximum790
Zeros13
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:05.490060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median19
Q332.25
95-th percentile76.25
Maximum790
Range790
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation42.61329216
Coefficient of variation (CV)1.562865492
Kurtosis208.5429536
Mean27.26612903
Median Absolute Deviation (MAD)10
Skewness12.16137931
Sum13524
Variance1815.892669
MonotonicityNot monotonic
2022-04-26T13:15:05.603596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1420
 
4.0%
1320
 
4.0%
2616
 
3.2%
1016
 
3.2%
1715
 
3.0%
1115
 
3.0%
1615
 
3.0%
714
 
2.8%
214
 
2.8%
1814
 
2.8%
Other values (78)337
67.4%
ValueCountFrequency (%)
013
2.6%
111
2.2%
214
2.8%
310
2.0%
47
1.4%
59
1.8%
610
2.0%
714
2.8%
811
2.2%
912
2.4%
ValueCountFrequency (%)
7901
0.2%
2081
0.2%
1811
0.2%
1471
0.2%
1391
0.2%
1281
0.2%
1231
0.2%
1221
0.2%
1211
0.2%
1091
0.2%

Total Interactions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct280
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.12
Minimum0
Maximum6334
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-04-26T13:15:05.714494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.95
Q171
median123.5
Q3228.5
95-th percentile605.85
Maximum6334
Range6334
Interquartile range (IQR)157.5

Descriptive statistics

Standard deviation380.233118
Coefficient of variation (CV)1.7925378
Kurtosis138.71578
Mean212.12
Median Absolute Deviation (MAD)67.5
Skewness9.712906039
Sum106060
Variance144577.224
MonotonicityNot monotonic
2022-04-26T13:15:05.829412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756
 
1.2%
396
 
1.2%
06
 
1.2%
675
 
1.0%
905
 
1.0%
1175
 
1.0%
975
 
1.0%
1264
 
0.8%
1214
 
0.8%
174
 
0.8%
Other values (270)450
90.0%
ValueCountFrequency (%)
06
1.2%
23
0.6%
32
 
0.4%
42
 
0.4%
52
 
0.4%
63
0.6%
74
0.8%
81
 
0.2%
92
 
0.4%
104
0.8%
ValueCountFrequency (%)
63341
0.2%
21771
0.2%
19741
0.2%
18731
0.2%
18061
0.2%
17771
0.2%
16261
0.2%
14391
0.2%
12901
0.2%
11741
0.2%

Interactions

2022-04-26T13:15:01.100133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:48.694027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.948939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.115713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.498390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.834597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.336927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.785063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.213539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.606164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.219728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:48.827077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.083799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.358515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.642705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.976380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.458019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.921913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.320282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.717980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.336082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:48.961837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.208147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.479797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.778111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.129596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.599389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.070665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.452637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.850430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.466423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.106456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.319676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.607754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.894699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.250180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.743411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.206282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.589516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.034608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.623825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.240580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.450253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.728804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.043248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.389419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.879461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.353662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.857188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.204402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.763067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.361262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.561185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.869738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.156173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.519373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.013472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.456237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.005574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.353610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:01.905267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.477152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.685750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.001412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.302565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.666229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.179757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.588157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.140382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.532737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:02.029658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.597497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.797233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.122900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.416569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:54.784290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.310847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.725061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.274624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.657509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:02.175763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.699545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:50.897127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.235949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.561770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.065616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.441931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:57.888786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.378331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.801123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:02.290715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:49.818887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:51.011504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:52.362881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:53.689389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:55.215376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:56.621896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:58.050928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:14:59.488316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T13:15:00.954168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-26T13:15:05.925683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-26T13:15:06.106536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-26T13:15:06.276010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-26T13:15:06.473592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-26T13:15:06.572255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-26T13:15:02.664668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-26T13:15:02.912275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-26T13:15:03.052528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-26T13:15:03.145279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CategoryPost MonthPaidLifetime Post Total ReachLifetime Post Total ImpressionsLifetime Engaged UsersLifetime Post ConsumersLifetime Post ConsumptionsCommentsLikesSharesTotal Interactions
02120.027525091178109159479.017.0100
12120.010460190571457136116745130.029.0164
23120.024134373177113154066.014.080
32121.0501288799122117901119581572.0147.01777
42120.072441359467141058019325.049.0393
52120.010472208491191107313891152.033.0186
63121.011692194794812653643249.027.0279
73121.013720241375372323050325.014.0339
82120.011844225381530140716920161.031.0192
93120.0469486682801832503113.026.0142

Last rows

CategoryPost MonthPaidLifetime Post Total ReachLifetime Post Total ImpressionsLifetime Engaged UsersLifetime Post ConsumersLifetime Post ConsumptionsCommentsLikesSharesTotal Interactions
490310.0528087039519111237179.030.0110
491311.061841022895690111401105.046.0152
492110.04592058087536557630128.09.0137
493310.084121396011791111163217185.055.0257
494310.054009218810756100310125.041.0176
495310.046847536733708985553.026.084
496210.034806229537508687053.022.075
497110.037787216625572795493.018.0115
498310.041567564626574832791.038.0136
49921NaN41887292564524743091.028.0119